Research And Application Of Intelligent Evaluation Data Mining Based On Knowledge Graph

Data mining has emerged at the historic moment with the development of information technology,which provids a platform and foundation for the emergence of educational big data mining.Data mining analyzes the internal relationship between data.Online educational provides sufficient data support for educational big data mining,solves the problem that students' learning level cannot be accurately located,and provides targeted guidance and suggestions for teachers.Educational data mining analyzes the data generated in the education process to produce guidance methods for teachers,improve student learning interest and motivate students to learn.Aiming at educational informationization,educational visualization and intelligence,this article takes knowledge graph as visual tool in the personalized learning system.And research is carried out from the following aspects:(1)This paper proposes a student knowledge graph construction model based on text classification and cluster analysis in order to improve the analysis ability of online education and connect students with the same characteristics.The model uses a text classification algorithm to automatically classify the test questions into knowledge points,which simplifies the complicated manual classification.And the model fully analyzes the data generated in online assessment,extracts the learning characteristics of students,and uses clustering algorithms to cluster students with the same learning characteristics,extracts the relationships between students based on the students' answer data,generates student knowledge graph,it connects students and provides assistance for teachers to realize personalized teaching in the actual teaching process.(2)In order to dig students' evaluation data and provide learning recommendations for students,an automatic generation model of knowledge point recommendation routes based on knowledge graph is proposed.This model generates a knowledge point learning route,which is suitable for students' own learning characteristics.And it can provide reference for study duration.In order to locate the knowledge points that students need to improve,a simplified knowledge point recommendation route is generated,which recommends the most important knowledge points for students.In order to mine the learning characteristics of students of different genders,the students are analyzed according to their gender,and the personality characteristics and behavior habits that affect the degree of mastering of the students are analyzed,which provides targeted learning guidance for students of different genders and characteristics.This model effectively distinguishes students with different characteristics,and provides reference and guidance for students' learning.(3)Based on the above theoretical research results,a knowledge graph generation and knowledge point recommendation route analysis system based on students' evaluation data is developed,which applies the theoretical results to practice.The system effectively analyzes dynamic answer data that affects students' learning characteristics,automatic generate knowledge graph based on students' answer data,and customize knowledge point recommendation routes for students.The theoretical research results are applied to the teaching system in a visual form,and the teaching plan is designed according to the characteristics of the students to improve teaching effectiveness.